Multi-Network Feature Fusion Facial Emotion Recognition using Nonparametric Method with Augmentation

Facial expression emotion identification and prediction is one of the most difficult problems in computer science. Pre-processing and feature extraction are crucial components of the more conventional methods. For the purpose of emotion identification and prediction using 2D facial expressions, this study targets the Face Expression Recognition dataset and shows the real implementation or assessment of learning algorithms such as various CNNs. Due to its vast potential in areas like artificial intelligence, emotion detection from facial expressions has become an essential requirement. Many efforts have been done on the subject since it is both a challenging and fascinating challenge in computer vision. The focus of this study is on using a convolutional neural network supplemented with data to build a facial emotion recognition system. This method may use face images to identify seven fundamental emotions, including anger, contempt, fear, happiness, neutrality, sadness, and surprise. As well as improving upon the validation accuracy of current models, a convolutional neural network that takes use of data augmentation, feature fusion, and the NCA feature selection approach may assist solve some of their drawbacks. Researchers in this area are focused on improving computer predictions by creating methods to read and codify facial expressions. With deep learning's striking success, many architectures within the framework are being used to further the method's efficacy. We highlight the contributions dealt with, the architecture and databases used, and demonstrate the development by contrasting the offered approaches and the outcomes produced. The purpose of this study is to aid and direct future researchers in the subject by reviewing relevant recent studies and offering suggestions on how to further the field. An innovative feature-based transfer learning technique is created using the pre-trained networks MobileNetV2 and DenseNet-201. The suggested system's recognition rate is 75.31%, which is a significant improvement over the results of the prior feature fusion study

Análisis de tecnologías digitales para beneficiar el ejercicio profesional de los mediadores familiares de Chile

In this study, I analyze digital technologies that could benefit the profes­sional practice of family mediators in Chile. I supported the hypothesis that there are digital technologies that could contribute to safeguarding principles of mediation, favoring communication, managing emotions, and seeking agreements, in harmony with Chilean laws 19.968 and 19.628. The results show twelve digital technologies compatible with my hypothesis. I conclude that these technologies contribute to the professional practice of mediators but must not affect their overall performance and do not violate fundamental rights or the theory of mediation.En este estudio analizamos tecnologías digitales que pudieran beneficiar el ejercicio profesional de los mediadores familiares de Chile. Sostenemos la hipó­tesis de que existen tecnologías digitales que podrían contribuir a resguardar prin­cipios de la mediación, favorecer la comunicación, gestionar las emociones y la búsqueda de acuerdos, en armonía con las leyes 19.968 y 19.628 de Chile. Nuestros resultados muestran doce tecnologías digitales compatibles con nuestra hipótesis. Concluimos que estas tecnologías contribuyen al ejercicio profesional de los me­diadores, siempre que no afecten su desempeño global y no transgredan derechos fundamentales o la teoría de la mediación

Emotion and Stress Recognition Related Sensors and Machine Learning Technologies

This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective